Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks

Xufei Ge; Martin Given; Brian G. Stewart

doi:10.1109/ICHVE53725.2022.9961792

Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks

Xufei Ge, Martin Given, Brian G. Stewart

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

3 Citations (Scopus)

60 Downloads (Pure)

Abstract

The spatial temperature profile of Medium-Voltage (MV) extruded power cable, undergoing accelerated aging in a tank filled with water according to IEEE standard 1407 guidance, is estimated by combining Finite-Element Modelling (FEM) and an Artificial Neural Network (ANN). ANSYS Fluent is first used to establish a 3-D finite-element model and to simulate the temperature distribution within the power cable. In order to estimate temperature at any position within the power cable thus informing a more accurate aging model based on the variation temperature with position, a 3 layers ANN, trained by Bayesian regularization back-propagation is then developed. For the ANN, the FEA simulation temperature profiles at specified nodes are used as the input information. The resulting model is useful to understand how each position within a cable undergoing artificial aging is affected by different temperatures.

Original language	English
Title of host publication	2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)
Place of Publication	Piscataway, NJ.
Publisher	IEEE
Pages	1-4
Number of pages	4
ISBN (Print)	9781665407519
DOIs	https://doi.org/10.1109/ICHVE53725.2022.9961792
Publication status	Published - 25 Sept 2022
Event	International Conference on High Voltage Engineering - Chongqing, Chongqing, China Duration: 25 Sept 2022 → 29 Sept 2022 http://www.ichve2022.org/

Publication series

Name	2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)
Publisher	IEEE

Conference

Conference	International Conference on High Voltage Engineering
Abbreviated title	ICHVE 2022
Country/Territory	China
City	Chongqing
Period	25/09/22 → 29/09/22
Internet address	http://www.ichve2022.org/

Keywords

power cables
tempertaure
Artificial Neural Networks (ANNs)
medium voltage (MV)
finite element analysis (FEA)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICHVE53725.2022.9961792

Ge-etal-ICHVE-2022-Determining-accelerated-aging-power-cable-spatial-temperature-profilesAccepted author manuscript, 1.01 MBLicence: Strath_1

3 Citations
2 Paper

XLPE cable insulation resistance modelling under annealing and thermal ageing effects
Ge, X., Fan, F., Given, M. J. & Stewart, B. G., 19 Oct 2023, p. 1-4. 4 p.
Research output: Contribution to conference › Paper › peer-review

Open Access
File
A power cable thermal aging insulation resistance degradation model
Ge, X., Given, M. & Stewart, B. G., 30 Oct 2022, p. 1-4. 4 p.
Research output: Contribution to conference › Paper › peer-review

Open Access
File

Cite this

Ge, X., Given, M., & Stewart, B. G. (2022). Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks. In 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE) (pp. 1-4). (2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)). IEEE. https://doi.org/10.1109/ICHVE53725.2022.9961792

Ge, Xufei ; Given, Martin ; Stewart, Brian G. / Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks. 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). Piscataway, NJ. : IEEE, 2022. pp. 1-4 (2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)).

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title = "Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks",

abstract = "The spatial temperature profile of Medium-Voltage (MV) extruded power cable, undergoing accelerated aging in a tank filled with water according to IEEE standard 1407 guidance, is estimated by combining Finite-Element Modelling (FEM) and an Artificial Neural Network (ANN). ANSYS Fluent is first used to establish a 3-D finite-element model and to simulate the temperature distribution within the power cable. In order to estimate temperature at any position within the power cable thus informing a more accurate aging model based on the variation temperature with position, a 3 layers ANN, trained by Bayesian regularization back-propagation is then developed. For the ANN, the FEA simulation temperature profiles at specified nodes are used as the input information. The resulting model is useful to understand how each position within a cable undergoing artificial aging is affected by different temperatures.",

keywords = "power cables, tempertaure, Artificial Neural Networks (ANNs), medium voltage (MV), finite element analysis (FEA)",

author = "Xufei Ge and Martin Given and Stewart, {Brian G.}",

year = "2022",

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publisher = "IEEE",

pages = "1--4",

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note = "International Conference on High Voltage Engineering, ICHVE 2022 ; Conference date: 25-09-2022 Through 29-09-2022",

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Ge, X , Given, M & Stewart, BG 2022, Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks. in 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE), IEEE, Piscataway, NJ., pp. 1-4, International Conference on High Voltage Engineering, Chongqing, China, 25/09/22. https://doi.org/10.1109/ICHVE53725.2022.9961792

Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks. / Ge, Xufei ; Given, Martin ; Stewart, Brian G.
2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). Piscataway, NJ.: IEEE, 2022. p. 1-4 (2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks

AU - Ge, Xufei

AU - Given, Martin

AU - Stewart, Brian G.

PY - 2022/9/25

Y1 - 2022/9/25

N2 - The spatial temperature profile of Medium-Voltage (MV) extruded power cable, undergoing accelerated aging in a tank filled with water according to IEEE standard 1407 guidance, is estimated by combining Finite-Element Modelling (FEM) and an Artificial Neural Network (ANN). ANSYS Fluent is first used to establish a 3-D finite-element model and to simulate the temperature distribution within the power cable. In order to estimate temperature at any position within the power cable thus informing a more accurate aging model based on the variation temperature with position, a 3 layers ANN, trained by Bayesian regularization back-propagation is then developed. For the ANN, the FEA simulation temperature profiles at specified nodes are used as the input information. The resulting model is useful to understand how each position within a cable undergoing artificial aging is affected by different temperatures.

AB - The spatial temperature profile of Medium-Voltage (MV) extruded power cable, undergoing accelerated aging in a tank filled with water according to IEEE standard 1407 guidance, is estimated by combining Finite-Element Modelling (FEM) and an Artificial Neural Network (ANN). ANSYS Fluent is first used to establish a 3-D finite-element model and to simulate the temperature distribution within the power cable. In order to estimate temperature at any position within the power cable thus informing a more accurate aging model based on the variation temperature with position, a 3 layers ANN, trained by Bayesian regularization back-propagation is then developed. For the ANN, the FEA simulation temperature profiles at specified nodes are used as the input information. The resulting model is useful to understand how each position within a cable undergoing artificial aging is affected by different temperatures.

KW - power cables

KW - tempertaure

KW - Artificial Neural Networks (ANNs)

KW - medium voltage (MV)

KW - finite element analysis (FEA)

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DO - 10.1109/ICHVE53725.2022.9961792

M3 - Conference contribution book

SN - 9781665407519

T3 - 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)

SP - 1

EP - 4

BT - 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)

PB - IEEE

CY - Piscataway, NJ.

T2 - International Conference on High Voltage Engineering

Y2 - 25 September 2022 through 29 September 2022

ER -

Ge X , Given M , Stewart BG. Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks. In 2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE). Piscataway, NJ.: IEEE. 2022. p. 1-4. (2022 IEEE International Conference on High Voltage Engineering and Applications (ICHVE)). doi: 10.1109/ICHVE53725.2022.9961792

Determining accelerated aging power cable spatial temperature profiles using Artificial Neural Networks

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Research output

XLPE cable insulation resistance modelling under annealing and thermal ageing effects

A power cable thermal aging insulation resistance degradation model

Cite this